Comparison of artificial intelligence techniques for energy consumption estimation

Oludolapo Akanni Olanrewaju; Charles Mbohwa

doi:10.1109/EPEC.2016.7771702

Comparison of artificial intelligence techniques for energy consumption estimation

Oludolapo Akanni Olanrewaju, Charles Mbohwa

Quality and Operations Management

University of Johannesburg

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

Abstract

In this article, a comparison study of three artificial intelligence (AI) techniques for energy consumption estimation are presented. The models considered are: multilayer perceptron (MLP); radial basis function (RBF) and support vector machine (SVM). The energy consumption is modeled as a function of activity, structural and intensity changes. The models are applied to Canadian industrial manufacturing data from 1990 to 2000. Comparisons were based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Root Relative Square Error (RRSE) as well as Simulation Time. The best results were obtained for the Multilayer Perceptron.

Original language	English
Title of host publication	2016 IEEE Electrical Power and Energy Conference, EPEC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509019199
DOIs	https://doi.org/10.1109/EPEC.2016.7771702
Publication status	Published - 5 Dec 2016
Event	2016 IEEE Electrical Power and Energy Conference, EPEC 2016 - Ottawa, Canada Duration: 12 Oct 2016 → 14 Oct 2016

Publication series

Name	2016 IEEE Electrical Power and Energy Conference, EPEC 2016

Conference

Conference	2016 IEEE Electrical Power and Energy Conference, EPEC 2016
Country/Territory	Canada
City	Ottawa
Period	12/10/16 → 14/10/16

Keywords

Energy consumption
Multilayer perceptron
Radial basis function
Support vector regression

ASJC Scopus subject areas

Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Control and Optimization

Access to Document

10.1109/EPEC.2016.7771702

Cite this

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title = "Comparison of artificial intelligence techniques for energy consumption estimation",

abstract = "In this article, a comparison study of three artificial intelligence (AI) techniques for energy consumption estimation are presented. The models considered are: multilayer perceptron (MLP); radial basis function (RBF) and support vector machine (SVM). The energy consumption is modeled as a function of activity, structural and intensity changes. The models are applied to Canadian industrial manufacturing data from 1990 to 2000. Comparisons were based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Root Relative Square Error (RRSE) as well as Simulation Time. The best results were obtained for the Multilayer Perceptron.",

keywords = "Energy consumption, Multilayer perceptron, Radial basis function, Support vector regression",

author = "Olanrewaju, {Oludolapo Akanni} and Charles Mbohwa",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Electrical Power and Energy Conference, EPEC 2016 ; Conference date: 12-10-2016 Through 14-10-2016",

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Olanrewaju, OA & Mbohwa, C 2016, Comparison of artificial intelligence techniques for energy consumption estimation. in 2016 IEEE Electrical Power and Energy Conference, EPEC 2016., 7771702, 2016 IEEE Electrical Power and Energy Conference, EPEC 2016, Institute of Electrical and Electronics Engineers Inc., 2016 IEEE Electrical Power and Energy Conference, EPEC 2016, Ottawa, Canada, 12/10/16. https://doi.org/10.1109/EPEC.2016.7771702

Comparison of artificial intelligence techniques for energy consumption estimation. / Olanrewaju, Oludolapo Akanni; Mbohwa, Charles.
2016 IEEE Electrical Power and Energy Conference, EPEC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7771702 (2016 IEEE Electrical Power and Energy Conference, EPEC 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Comparison of artificial intelligence techniques for energy consumption estimation

AU - Olanrewaju, Oludolapo Akanni

AU - Mbohwa, Charles

PY - 2016/12/5

Y1 - 2016/12/5

N2 - In this article, a comparison study of three artificial intelligence (AI) techniques for energy consumption estimation are presented. The models considered are: multilayer perceptron (MLP); radial basis function (RBF) and support vector machine (SVM). The energy consumption is modeled as a function of activity, structural and intensity changes. The models are applied to Canadian industrial manufacturing data from 1990 to 2000. Comparisons were based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Root Relative Square Error (RRSE) as well as Simulation Time. The best results were obtained for the Multilayer Perceptron.

AB - In this article, a comparison study of three artificial intelligence (AI) techniques for energy consumption estimation are presented. The models considered are: multilayer perceptron (MLP); radial basis function (RBF) and support vector machine (SVM). The energy consumption is modeled as a function of activity, structural and intensity changes. The models are applied to Canadian industrial manufacturing data from 1990 to 2000. Comparisons were based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Root Relative Square Error (RRSE) as well as Simulation Time. The best results were obtained for the Multilayer Perceptron.

KW - Energy consumption

KW - Multilayer perceptron

KW - Radial basis function

KW - Support vector regression

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M3 - Conference contribution

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T3 - 2016 IEEE Electrical Power and Energy Conference, EPEC 2016

BT - 2016 IEEE Electrical Power and Energy Conference, EPEC 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE Electrical Power and Energy Conference, EPEC 2016

Y2 - 12 October 2016 through 14 October 2016

ER -

Comparison of artificial intelligence techniques for energy consumption estimation

Abstract

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Keywords

ASJC Scopus subject areas

Access to Document

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